The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity.
#Gps file depot smartphone windows
The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. The experiments have been video-recorded to label the data manually. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Universitat Politècnica de Catalunya (BarcelonaTech). Reyes-Ortiz(1,2), Davide Anguita(1), Alessandro Ghio(1), Luca Oneto(1) and Xavier Parra(2)ġ - Smartlab - Non-Linear Complex Systems LaboratoryĭITEN - Università degli Studi di Genova, Genoa (I-16145), Italy.Ģ - CETpD - Technical Research Centre for Dependency Care and Autonomous Living Human Activity Recognition Using Smartphones Data Setĭownload: Data Folder, Data Set DescriptionĪbstract: Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns.